Senior Director of Enterprise AI Platform Engineering

Join SentinelOne as the Senior Director of Enterprise AI Platform Engineering. In this role, you will build the AI foundation that supports all internal AI use cases. You will own and execute the roadmap for SentinelOne's enterprise AI platform, design and build the AI Gateway layer, and operate the Harness layer. You will also govern how agents interact with our systems, hire and mentor a team of engineers, and communicate platform status to executive stakeholders. - Construire la fondation de l'IA sur laquelle reposent tous les cas d'utilisation internes de l'IA. - Posséder et exécuter la feuille de route de la plateforme d'IA de l'entreprise de SentinelOne. - Concevoir et construire la couche de passerelle d'IA : identité centralisée et AuthN/Z, budgétisation des jetons, DLP et garde-fous de contenu. - We’re looking for people who are relentlessly curious and committed to continuous learning. AI is reshaping every function across our business, and we enable every team member, regardless of role or level, to build fluency in AI tools and concepts - Those who thrive here actively seek out new solutions, experiment thoughtfully, and apply what they learn to drive better, faster, smarter outcomes - Demonstrated experience designing and implementing an AI gateway — centralized auth, rate limiting, DLP, observability, and audit logging across multiple model providers. Hands-on experience with Kong AI Gateway specifically is a plus - You have experience building or owning a model evaluation framework — not just running benchmarks, but closing the loop: using eval results to drive model selection, prompt tuning, and routing decisions - Hands-on depth in agent orchestration (LangGraph or equivalent), including experience routing to hosted model providers such as AWS Bedrock and Google Vertex. You understand how context windows, memory, and tool-calling actually work — not just conceptually - Familiarity with the Model Context Protocol (MCP) and practical experience governing tool access for agents operating across enterprise systems in a secure way - 7+ years of software engineering experience, with at least 3 years in an engineering leadership role and at least 1 year leading a team building AI or ML infrastructure in a production enterprise environment - Experience evaluating and selecting between build, buy, and assemble options at the layer level — including vendor assessment, total cost of ownership modeling, and the discipline to not over-engineer where mature open-source or commercial components already exist - Strong software engineering background — you can write and review production code, understand API design, and hold a high technical bar with your team. Our current stack includes Python 3.11+ with FastAPI, React 18+ with TypeScript, and for LLM-powered components; we expect this leader to be comfortable working in that stack and to evaluate, evolve, and own those standards going forward - You have built at least one production platform — AI or otherwise — that enforced centralized policy across multiple consumers: auth/AuthN/Z, rate limiting, observability, and audit logging. Bonus if that platform touched AI specifically (LLM gateway, model routing, or orchestration), but strong candidates from API platform, data platform, or developer platform backgrounds are equally welcome. What matters is that you can describe what you built, the tradeoffs you made, and what you'd do differently - Hands-on familiarity with enterprise AI deployments — specifically Claude Enterprise (Anthropic) and Gemini Enterprise — including how they are administered, how access and policy controls work, and how they connect to a broader governed AI architecture - Demonstrated ability to collaborate across organizational boundaries — this role operates in tight partnership with Enterprise Apps, Data, Product Development, and Infosec teams. You have a track record of building shared architectural contracts